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A Comparison of Two Methods for Estimating DCE-MRI Parameters via Individual and Cohort Based AIFs in Prostate Cancer: A Step towards Practical Implementation

1Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address:
2Department of Radiology, Northwestern University, Chicago, IL, USA.
3Department of Biostatistics, Vanderbilt University, Nashville, TN, USA.
4Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
5General Electric Global Research, Niskayuna, NY, USA.
6Department of Radiology, Children's Hospital Boston, Harvard Medical School, Boston, MA, USA.
Elsevier Science
Publication Date:
Magn Reson Imaging
Volume Number:
Issue Number:
Magn Reson Imaging. 2014 May;32(4):321-9.
PubMed ID:
Arterial Input Function, DCE-MRI, Pharmacokinetic Modeling, Prostate Cancer, Quantitative Imaging
Appears in Collections:
Prostate Group, NCIGT, SLICER
U01 CA142565/CA/NCI NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
U01 CA142565/CA/NCI NIH HHS/United States
U01 CA151261/CA/NCI NIH HHS/United States
Generated Citation:
Fedorov A., Fluckiger J., Ayers G.D., Li X., Gupta S.N., Tempany C.M., Mulkern R.V., Yankeelov T.E., Fennessy F.M. A Comparison of Two Methods for Estimating DCE-MRI Parameters via Individual and Cohort Based AIFs in Prostate Cancer: A Step towards Practical Implementation. Magn Reson Imaging. 2014 May;32(4):321-9. PMID: 24560287. PMCID: PMC3965600.
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Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods. Two methods for automated image-based estimation of individualized (patient-specific) AIFs, one of which was previously validated for brain and the other for breast MRI, were compared. cAIFs were constructed by averaging the iAIF curves over the individual patients for each of the two methods. Pharmacokinetic analysis using the Generalized kinetic model and each of the four AIF choices (iAIF and cAIF for each of the two image-based AIF estimation approaches) was applied to derive the volume transfer rate (Ktrans) and extravascular extracellular volume fraction (ve) in the areas of prostate tumor. Differences between the parameters obtained using iAIF and cAIF for a given method (intra-method comparison) as well as inter-method differences were quantified. The study utilized DCE MRI data collected in 17 patients with histologically confirmed PCa. Comparison at the level of the tumor region of interest (ROI) showed that the two automated methods resulted in significantly different (p<0.05) mean estimates of ve, but not of Ktrans. Comparing cAIF, different estimates for both ve, and Ktrans were obtained. Intra-method comparison between the iAIF- and cAIF-driven analyses showed the lack of effect on ve, while Ktrans values were significantly different for one of the methods. Our results indicate that the choice of the algorithm used for automated image-based AIF determination can lead to significant differences in the values of the estimated PK parameters. Ktrans estimates are more sensitive to the choice between cAIF/iAIF as compared to ve, leading to potentially significant differences depending on the AIF method. These observations may have practical consequences in evaluating the PK analysis results obtained in a multi-site setting.

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